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AI is visible. If it is not on the box of a CE product, it will most likely be mentioned in the promotional literature, the Set-up guide, or ‘Controls & Instructions”, and will certainly be mentioned (many times) in the advertisements promoting that particular CE device. In fact it would likely be more difficult to find premium CE products that don’t mention AI, but aside from the chatbots and LLMs that are typically the face of AI, much of what we are told is AI is not seen but can still affect many of the device functions we use on a daily basis. Smartphones are obvious AI candidates and the competition for ‘AIness’ is already underway, as it is in laptops, desktop computers, and even in tablets, but what about TV sets? One doesn’t typically query your TV about “What’s happening at the Diddy trial?” and rarely does searches on your TV, other than when looking for a show or movie, but AI does play a part in the TV set space, even though you don’t see it at work.
It goes deeper. Some TV set AI functions, while not controlled by the viewer, can be seem by the viewer. Video processors in premium TVs are no longer just perform the same mathematical functions on each frame. Now they adapt how they process images based on a neural model that was rained for that specific purpose while other Ai functions are totally invisible to the user, although they are able to influence users in other ways. Non-AI image processors are traditionally rule-based (Think of a recipe for Lasagna). They use fixed algorithms based on well-established formulas and are applied uniformly across images or sections of images. They typically work toward improving picture sharpness, enhancing edge definition, and reducing noise by working on small squares (3x3, 5x5, etc.) of pixels. AI image processors are constantly adapting to actual conditions (Think of a chef who constantly modifies a recipe to improve it).
As much of the content we watch is not high resolution, TV set processors spend considerable processing power trying to upscale video for current 4K displays, essentially trying to make a low resolution image look better on a high resolution display. Typically this is done through one of three processes.
Bilinear interpolation – This function averages the 4 nearest pixel values to create a new pixel value to fill in extra pixels. The result is a soft, blurrier image.
Bicubic interpolation – This function uses a larger block of pixels (16) and a more complex polynomial function to create new pixels. The result is a sharper image but can create artifacts.
Nearest Neighbor – This function just copies the values of the nearest pixel. It produces a blocky image but is the fastest of all.
There are also global functions, such as those that adjust the overall contrast of a scene based on the darkest and lightest parts, but older video processors lack the ability to understand the details, and processes like general noise reduction can also reduce detail. Users typically have to adjust settings for different types of content (movies, sports, news, etc.) as the functions of typical TV set video processors are generic and unable to easily adapt to differing content or environmental conditions.
AI image processors are different, and while each brand has their own AI processor with its own characteristics, here is what sets them apart from the generic processors that are and have been used in TV sets.
Convolutional Neural Networks – These processors use deep learning networks, with CNNs trained on very large datasets of images and video that allows them to learn complex relationships. Some of the training material consists of image or video pairs, with one being low resolution and the other high resolution. When comparing the pair on a pixel by pixel, square by square and segment by segment basis, the CNN is able to develop the same, “This is the most likely next pixel” process that a trained LLM would use to figure the next token in a sentence. By using this technique, it generates the ‘most likely’ pixel to fill in blanks when upscaling. While these pixel choices are being made, the AI is also evaluating the scene on a frame by frame basis to better understand the image flow. This can help the Ai to identify the content type (movie, sports, news, etc.) and make adjustments to match. It can even go as far as recognizing objects in a frame in order to independently adjust their characteristics on the fly.
Depending on the AI, and its capabilities, the AI processor can also perform highly accurate color tone mapping, keeping object details accurate in both light and dark situations, essentially creating an HDR-like image from SDR content. The AI can also be used for noise and artifact reduction, having been trained to recognize the difference between various types of noise (film grain as opposed to digital noise) and can even clean up motion blur that occurs with fast moving objects in lower resolution sports content.
Similarly AI processing can also be applied to TV sound where acoustic tuning, adaptive surround sound, and dialogue enhancement are all able to be accomplished using highly trained ML systems, and voice controlled remotes will improve greatly as AI LLMs better understand the context of commands. But what about those mystery AI functions, the ones that work completely away from the user? We know that many TVs track user viewing habits and that data is ripe for Ai’s to use for content recommendations. Rather than the endless searching for attractive content that users wind up doing, Ai systems are easily tasked with real-time searches that are adapted to the user’s habits or past requests, but here’s one function that we can safely say would be a pleasure when implemented…live translation. Instantaneous on-screen sub-title translations, and eventually (not that far off) direct translation to match the image without sub-titles, essentially lip-syncing the translation to the image.
There are a host of other TV set features, many of which are embedded in process applications that AI can improve, and we haven’t even touched on gaming, where there are many features that AI can improve by ‘learning’ from its users, but we would also be remiss if we did not mention the fact that along with the improvements in TV sets that AI is bringing comes a decrease in privacy and security. ACR (Automatic Content Recognition) systems are always running when the set is on and identify all content, regardless of source, being displayed on the screen by capturing screenshots of content roughly two times each second. Those screenshots are cross-referenced against a huge database of media content and advertising, providing a rich history of the viewer’s habits that is used to enhance content recommendations but can also be sold to marketers, especially given how difficult TV set brands make it to disable the feature. Passing this user information to an AI system, adapted to spot patterns and nuance, such systems would open the user to serious privacy concerns.
But when it comes down to the pluses and minuses, AI has the ability to improve TV set performance significantly and given the still early stage of AI and ML systems, brands have barely scratched the surface. Brands are still experimenting with AI and applied AI processes to some of the more obvious functions we have mentioned. As these processors improve, we expect there to be a shift from the more typical ‘Bigger is better” and “Higher resolution” competition to “…can your TV do this?”. Differentiation is the name of the game in the CE space and feature cycles can dry up in an instant, so AI features, some of which the user may have to take the brand’s word for, open up a whole treasure trove of marketable features. What brand would not look forward to that?
It goes deeper. Some TV set AI functions, while not controlled by the viewer, can be seem by the viewer. Video processors in premium TVs are no longer just perform the same mathematical functions on each frame. Now they adapt how they process images based on a neural model that was rained for that specific purpose while other Ai functions are totally invisible to the user, although they are able to influence users in other ways. Non-AI image processors are traditionally rule-based (Think of a recipe for Lasagna). They use fixed algorithms based on well-established formulas and are applied uniformly across images or sections of images. They typically work toward improving picture sharpness, enhancing edge definition, and reducing noise by working on small squares (3x3, 5x5, etc.) of pixels. AI image processors are constantly adapting to actual conditions (Think of a chef who constantly modifies a recipe to improve it).
As much of the content we watch is not high resolution, TV set processors spend considerable processing power trying to upscale video for current 4K displays, essentially trying to make a low resolution image look better on a high resolution display. Typically this is done through one of three processes.
Bilinear interpolation – This function averages the 4 nearest pixel values to create a new pixel value to fill in extra pixels. The result is a soft, blurrier image.
Bicubic interpolation – This function uses a larger block of pixels (16) and a more complex polynomial function to create new pixels. The result is a sharper image but can create artifacts.
Nearest Neighbor – This function just copies the values of the nearest pixel. It produces a blocky image but is the fastest of all.
There are also global functions, such as those that adjust the overall contrast of a scene based on the darkest and lightest parts, but older video processors lack the ability to understand the details, and processes like general noise reduction can also reduce detail. Users typically have to adjust settings for different types of content (movies, sports, news, etc.) as the functions of typical TV set video processors are generic and unable to easily adapt to differing content or environmental conditions.
AI image processors are different, and while each brand has their own AI processor with its own characteristics, here is what sets them apart from the generic processors that are and have been used in TV sets.
Convolutional Neural Networks – These processors use deep learning networks, with CNNs trained on very large datasets of images and video that allows them to learn complex relationships. Some of the training material consists of image or video pairs, with one being low resolution and the other high resolution. When comparing the pair on a pixel by pixel, square by square and segment by segment basis, the CNN is able to develop the same, “This is the most likely next pixel” process that a trained LLM would use to figure the next token in a sentence. By using this technique, it generates the ‘most likely’ pixel to fill in blanks when upscaling. While these pixel choices are being made, the AI is also evaluating the scene on a frame by frame basis to better understand the image flow. This can help the Ai to identify the content type (movie, sports, news, etc.) and make adjustments to match. It can even go as far as recognizing objects in a frame in order to independently adjust their characteristics on the fly.
Depending on the AI, and its capabilities, the AI processor can also perform highly accurate color tone mapping, keeping object details accurate in both light and dark situations, essentially creating an HDR-like image from SDR content. The AI can also be used for noise and artifact reduction, having been trained to recognize the difference between various types of noise (film grain as opposed to digital noise) and can even clean up motion blur that occurs with fast moving objects in lower resolution sports content.
Similarly AI processing can also be applied to TV sound where acoustic tuning, adaptive surround sound, and dialogue enhancement are all able to be accomplished using highly trained ML systems, and voice controlled remotes will improve greatly as AI LLMs better understand the context of commands. But what about those mystery AI functions, the ones that work completely away from the user? We know that many TVs track user viewing habits and that data is ripe for Ai’s to use for content recommendations. Rather than the endless searching for attractive content that users wind up doing, Ai systems are easily tasked with real-time searches that are adapted to the user’s habits or past requests, but here’s one function that we can safely say would be a pleasure when implemented…live translation. Instantaneous on-screen sub-title translations, and eventually (not that far off) direct translation to match the image without sub-titles, essentially lip-syncing the translation to the image.
There are a host of other TV set features, many of which are embedded in process applications that AI can improve, and we haven’t even touched on gaming, where there are many features that AI can improve by ‘learning’ from its users, but we would also be remiss if we did not mention the fact that along with the improvements in TV sets that AI is bringing comes a decrease in privacy and security. ACR (Automatic Content Recognition) systems are always running when the set is on and identify all content, regardless of source, being displayed on the screen by capturing screenshots of content roughly two times each second. Those screenshots are cross-referenced against a huge database of media content and advertising, providing a rich history of the viewer’s habits that is used to enhance content recommendations but can also be sold to marketers, especially given how difficult TV set brands make it to disable the feature. Passing this user information to an AI system, adapted to spot patterns and nuance, such systems would open the user to serious privacy concerns.
But when it comes down to the pluses and minuses, AI has the ability to improve TV set performance significantly and given the still early stage of AI and ML systems, brands have barely scratched the surface. Brands are still experimenting with AI and applied AI processes to some of the more obvious functions we have mentioned. As these processors improve, we expect there to be a shift from the more typical ‘Bigger is better” and “Higher resolution” competition to “…can your TV do this?”. Differentiation is the name of the game in the CE space and feature cycles can dry up in an instant, so AI features, some of which the user may have to take the brand’s word for, open up a whole treasure trove of marketable features. What brand would not look forward to that?
Sources:
https://www.samsung.com/us/televisions-home-theater/tvs/ai-tvs/
https://www.advanced-television.com/2025/03/26/the-future-of-television-how-technology-is-shaping-the-industry/
https://www.trendhunter.com/trends/copilot-ai-for-tvs
https://www.reliant.co.uk/blog/how-does-ai-improve-the-sound-of-a-tv/
https://www.lge360.com/iraq_en/lg-story/helpful-guide/ai-tv-vs-traditional-tv
https://insights.samsung.com/2025/06/03/how-does-ai-upscaling-work-on-direct-view-led-displays-2/
https://www.lg.com/us/experience/ai-tv-for-sound-and-vision
https://www.samsung.com/us/televisions-home-theater/tvs/ai-tvs/
https://www.advanced-television.com/2025/03/26/the-future-of-television-how-technology-is-shaping-the-industry/
https://www.trendhunter.com/trends/copilot-ai-for-tvs
https://www.reliant.co.uk/blog/how-does-ai-improve-the-sound-of-a-tv/
https://www.lge360.com/iraq_en/lg-story/helpful-guide/ai-tv-vs-traditional-tv
https://insights.samsung.com/2025/06/03/how-does-ai-upscaling-work-on-direct-view-led-displays-2/
https://www.lg.com/us/experience/ai-tv-for-sound-and-vision
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